A medical image processing unit that visualizes information about the inside of a human body has been being rapidly widespread in the medical field and is used for various diagnoses. Examples of modalities used for the medical image processing unit are X-ray CT (Computed Tomography), PET (Positron Emission Tomography), MRI (Magnetic Resonance Imaging) and US (Ultrasonography), and these modalities can obtain three-dimensional volume data.
Doctors can diagnose the presence or absence of a liver cancer and progression of the cancer by using the X-ray CT as a modality and visualizing volume data of the liver, which is acquired by the X-ray CT, as an image.
Furthermore, in recent years the development of an automatic detector (hereinafter sometimes referred to as “CAD” [Computer Aided Diagnosis]) that automatically detects diseases represented by, for example, cancers from the volume data has been promoted for the purpose of reducing burdens on doctors. However, since the inside of a human body has very complicated structures, there may be errors in detection results acquired by using the automatic detector. Furthermore, noises peculiar to certain modalities may be mixed in the volume data depending on the modalities and some noises make it difficult for the automatic detector to automatically detect the diseases. Therefore, currently there is a strong demand for the development of an automatic detector capable of detecting the diseases as correctly as the doctors would detect. It should be noted that the technique detecting an object from the volume data is disclosed in, for example, PTL 1 and PTL 2 below.